AIMC Topic: Algorithms

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Human activity recognition algorithms for manual material handling activities.

Scientific reports
Human Activity Recognition (HAR) using wearable sensors has prompted substantial interest in recent years due to the availability and low cost of Inertial Measurement Units (IMUs). HAR using IMUs can aid both the ergonomic evaluation of the performed...

A hybrid approach with metaheuristic optimization and random forest in improving heart disease prediction.

Scientific reports
Cardiovascular diseases (CVD)  a major cause of morbidity and mortality among the world's non-communicable disease incidences. Though these practices are in use for diagnostics of different CVDs in clinical settings, need improvement because they are...

Exploring the Capacity of Large Language Models to Assess the Chronic Pain Experience: Algorithm Development and Validation.

Journal of medical Internet research
BACKGROUND: Chronic pain, affecting more than 20% of the global population, has an enormous pernicious impact on individuals as well as economic ramifications at both the health and social levels. Accordingly, tools that enhance pain assessment can c...

Optimizing Initial Vancomycin Dosing in Hospitalized Patients Using Machine Learning Approach for Enhanced Therapeutic Outcomes: Algorithm Development and Validation Study.

Journal of medical Internet research
BACKGROUND: Vancomycin is commonly dosed using standard weight-based methods before dose adjustments are made through therapeutic drug monitoring (TDM). However, variability in initial dosing can lead to suboptimal therapeutic outcomes. A predictive ...

Automatic Human Embryo Volume Measurement in First Trimester Ultrasound From the Rotterdam Periconception Cohort: Quantitative and Qualitative Evaluation of Artificial Intelligence.

Journal of medical Internet research
BACKGROUND: Noninvasive volumetric measurements during the first trimester of pregnancy provide unique insight into human embryonic growth and development. However, current methods, such as semiautomatic (eg, virtual reality [VR]) or manual segmentat...

Impact of Tumor Location on Predicting Early-Stage Breast Cancer Patient Survivability Using Explainable Machine Learning Models.

JCO clinical cancer informatics
PURPOSE: This study aims to investigate the impact of tumor quadrant location on the 5-year early-stage breast cancer survivability prediction using explainable machine learning (ML) models. By integrating these predictive models with Shapley Additiv...

Machine learning algorithms applied to the diagnosis of COVID-19 based on epidemiological, clinical, and laboratory data.

Jornal brasileiro de pneumologia : publicacao oficial da Sociedade Brasileira de Pneumologia e Tisilogia
OBJECTIVE: To predict COVID-19 in hospitalized patients with SARS in a city in southern Brazil by using machine learning algorithms.

Analysis of Deep Learning Techniques for Vehicle Detection and Reidentification Using Data from Multiple Drones and Public Datasets.

Anais da Academia Brasileira de Ciencias
The detection and re-identification of vehicles in dynamic environments, such as highways monitored by a swarm of drones, presents significant challenges, particularly due to the variability of images captured from different angles and under various ...

An Early Thyroid Screening Model Based on Transformer and Secondary Transfer Learning for Chest and Thyroid CT Images.

Technology in cancer research & treatment
IntroductionThyroid cancer is a common malignant tumor, and early diagnosis and timely treatment are crucial to improve patient prognosis. With the increasing use of enhanced CT scans, a new opportunity for early thyroid cancer screening has emerged....

Automated Autism Assessment With Multimodal Data and Ensemble Learning: A Scalable and Consistent Robot-Enhanced Therapy Framework.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Navigating the complexities of Autism Spectrum Disorder (ASD) diagnosis and intervention requires a nuanced approach that addresses both the inherent variability in therapeutic practices and the imperative for scalable solutions. This paper presents ...